SIGN LANGUAGE FINGER ALPHABET RECOGNITION FROM GABOR-PCA REPRESENTATION OF HAND GESTURES
During recent years a large number of computer aided applications have been developed to help the disabled people.This has improved the communication between the able and the bearing impaired community.An intelligent signed alphabet recognizer can work as an aiding agent to translate the signs to words (and also sentences) and vice versa.To achieve this goal few steps to be followed, among which the first complicated task is to recognize the sign-language alphabets from hand gesture images.In this paper, we propose a system that is able to recognize American Sign Language (ASL) alphabets from hand gesture with average 93.13％ accuracy.The classification Is performed with fuzzy-c-mean clustering on a lower dimensional data which is acquired from the Principle Component Analysis (PCA) of Gabor representation of hand gesture images.Out of the top 20 Principle Components (PCs) the best combination of PCs is determined by finding the best fuzzy cluster for the corresponding PCs of the training data.The best result is obtained from the combination of the fourth to seventh principle components.
Sign language Finger alphabet recognition Gabor wavelets PCA Clustering algorithm.
M.ASHRAFUL AMIN HONG YAN
Department of Electronic Engineering, City University of Hong Kong, Hong Kong Department of Electronic Engineering, City University of Hong Kong, Hong Kong;School of Electrical &
国际会议
2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)
香港
英文
2218-2223
2007-08-19（万方平台首次上网日期，不代表论文的发表时间）